Robust Control of Wireless Power Transfer Despite Load and Data Communications Uncertainties
Bibliographic record
Abstract
This article focuses on the robust control of wireless power transfer (WPT) systems, to work satisfactorily around the desired resonant frequency in the presence of load and data communications uncertainties. The proposed robust control system is based on the quantitative feedback theory (QFT), consisting of a feedback compensator and a prefilter, which are designed by shaping the system's frequency responses, to satisfy the design constraints defined in terms of stability, tracking, and other desired requirements. A feature of QFT is to provide the designer with interactive graphical tools for the design and tuning of the feedback compensator and prefilter. Without loss of generality, this article elaborates the design of the QFT-based robust control for a WPT system with a full-bridge inverter and series-series capacitor-based compensation circuits and uncertain direct-current (dc) load. The data communications uncertainties are also addressed in the design. The effectiveness of the proposed QFT-based robust control methodology is evaluated through simulations and practical experiments and compared with H∞and Skogestad internal model control (SIMC) design methods. Since QFT is a model-based approach, this article also elaborates on small-signal transfer function modeling of WPT systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".